Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-15 20:51 UTC

qBraid / qBraid

A platform-agnostic quantum runtime framework

PythonApache-2.0★ 117 stars⑂ 66 forkssince Sep 2019View on GitHub ↗
KindPluginLibraryhow this is determined
#openqasm#python#quantum#quantum-computing#transpiler#qbraid#qir#runtime#rustworkx

qBraid/qBraid holds a health index of 95 out of 100, placing it in the Exceptional band. It scores highest on Vitality (95/100) and lowest on Community & Adoption (70/100). It was last updated today. A single contributor accounts for most of its recent work.

95
overall / 100
Exceptional

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

95
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 82 is calibrated to 95 on the published index scale (record calibration 2026-08-02).

Ownership

qBraidOrganization · verified domain
188 followers66 public repossince Jan 2020

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIqbraid0.13.015,08621513 days agoqbraidquantumopenqasmruntime

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

95Exceptional · 21% of overall

Development activity

92Excellent
How it's scored
36/36Push recency — last push 0 days ago
28.4/36Commit cadence — 41/52 weeks with commits
18/18Commit volume — 254 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 24 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year254
human_commit_share0.9
days_since_last_push0
active_weeks_last_year41

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 53 releases published
36/36Release recency — latest release 13 days ago
27/27Release cadence — a release every ~42.3 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count53
latest_release_tagv0.13.0
releases_from_tagsno
days_since_latest_release13
mean_days_between_releases42.3
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

70Good · 17% of overall

Popularity & adoption

51Moderate
How it's scored
33.5/60Stars — 117 stars
15.1/25Forks — 66 forks
2.7/15Watchers — 4 watchers
Inputs used
forks66
stars117
watchers4
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges9
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicescodecov.io, github.com, shields.io
has_pull_request_templateyes

Ecosystem adoption (downloads)

70Good
How it's scored
55.7/80Monthly downloads — 15,086 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesqbraid
dependents—
ecosystemspypi
total_downloads—
monthly_downloads15,086
unverified_packages_excluded—
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

73Good · 23% of overall

Maintainer resilience (bus factor)

37Weak
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
4.5/22.5Commit distribution — top contributor authored 80% of commits
13.5/13.5Contributor breadth — 47 contributors
10/10OpenSSF Scorecard: Contributors — project has 13 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled47
top_contributor_share0.802

Issue & PR responsiveness

76Good
How it's scored
38.5/42Issue resolution — 92% of issues closed
23.5/30PR acceptance — 893/1,138 decided PRs merged
13/13Newcomer PR acceptance — 2/2 first-time contributors' PRs merged in 30d
1.5/15OpenSSF Scorecard: Code-Review — Found 3/29 approved changesets -- score normalized to 1
Inputs used
merged_prs893
open_issues26
closed_issues289
prs_merged_7d7
prs_decided_7d7
prs_merged_30d40
prs_decided_30d41
issue_closed_ratio0.917
closed_unmerged_prs245
first_time_authors_30d1
first_time_prs_merged_30d2
first_time_prs_decided_30d2

Ownership & stewardship

91Excellent
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
16.4/25Owner reach — 188 followers of qBraid
25/25Track record — 66 public repos, account ~6 yr old
Inputs used
followers188
owner_typeOrganization
is_verifiedyes
owner_loginqBraid
public_repos66
account_age_days2,454

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 13 days ago
20/20Version history — 215 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesqbraid
ecosystemspypi
any_deprecatedno
min_days_since_publish13

Engineering Quality

Are baseline engineering and documentation practices in place?

90Excellent · 19% of overall

Engineering practices

84Excellent
How it's scored
24/24CI workflows — 13 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff], [tool.pylint], [tool.black], [tool.isort]), tox.ini
0/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://docs.qbraid.com/v2/sdk
10/10Repository description
10/10Topics — 9 topics
10/10Wiki
Inputs used
topicsopenqasm, python, quantum, quantum-computing, transpiler, qbraid, qir, runtime, rustworkx
has_wikiyes
homepagehttps://docs.qbraid.com/v2/sdk
docs_sitehttps://docs.qbraid.com/v2/sdk
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

84Excellent · 16% of overall

Security posture

80Excellent
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
2.5/2.5CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0.8/7.5Code-Review — Found 3/29 approved changesets -- score normalized to 1
2.5/2.5Contributors — project has 13 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 24 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
1.5/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 3
4.5/5SAST — SAST tool is not run on all commits -- score normalized to 9
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — no data
7.5/7.5Token-Permissions — GitHub workflow tokens follow principle of least privilege
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate8
Excluded from scoring (no data or not applicable): Branch-Protection, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages19
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:qbraid@0.13.0 runtime dependency closure — what installing the published package pulls in — 19 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

78Good · 4% of overall

Agent context & guidance

55Moderate
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://docs.qbraid.com/llms.txt)
40/40Legible commit history — 90 of 90 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://docs.qbraid.com/llms.txt
legible_history_share1
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

93Exceptional
How it's scored
18/18One-command bootstrap — docs/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff], [tool.pylint], [tool.black], [tool.isort]), tox.ini
11/11Static type checking — qbraid/py.typed
10/10Reproducible environment — devcontainer
10/10Demonstrated agent practice — 7 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 9 of the last 100 commits are automated dependency updates
3/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 3
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_filesdocs/Makefile
has_devcontaineryes
has_linter_configyes
typecheck_configsqbraid/py.typed
agent_commit_share0.07
toolchain_manifests—
dependency_bot_commit_share0.09

Code legibility for models

82Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (qbraid/py.typed)
54.5/55Manageable file sizes — 4/401 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes515,389
source_files_sampled401
oversized_source_files4

Key facts

117GitHub stars
47contributors
254commits, last 12 months
0days since last push
53releases
1bus factor
26open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 66 ⇿
0Stars
66Forks
46Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

01325385063756632023-052025-012026-09
Major 0Minor 9Patch 37

Each point covers 4 days.

OpenSSF Scorecard 8.0 / 10
8.0aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-10-15 20:50 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
10CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
1Code-ReviewFound 3/29 approved changesets -- score normalized to 1
10Contributorsproject has 13 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 24 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
3Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 3
9SASTSAST tool is not run on all commits -- score normalized to 9
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
10Vulnerabilities0 existing vulnerabilities detected
All dependencies 63

Full resolved dependency set from the GitHub dependency graph: 0 direct and 63 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIamazon-braket-pennylane-plugin—indirect
PyPIamazon-braket-sdk—indirect
PyPIaqt-connector—indirect
PyPIattrs—indirect
PyPIautoqasm—indirect
PyPIazure-identity—indirect
PyPIazure-quantum—indirect
PyPIbloqade-analog—indirect
PyPIcirq-core—indirect
PyPIcirq-ionq—indirect
PyPIcudaq—indirect
PyPIdocutils—indirect
PyPIicalendar—indirect
PyPIipython—indirect
PyPImatplotlib—indirect
PyPImimiq-qiskit—indirect
PyPImimiqcircuits—indirect
PyPImyqlm-interop—indirect
PyPInumpy—indirect
PyPIopenqasm3—indirect
PyPIoqc-qcaas-client—indirect
PyPIpackaging—indirect
PyPIpasqal-cloud—indirect
PyPIpennylane—indirect
PyPIpennylane-cirq—indirect
PyPIpennylane-qiskit—indirect
PyPIply—indirect
PyPIpulser-core—indirect
PyPIpulser-simulation—indirect
PyPIpydantic—indirect
PyPIpydantic-core—indirect
PyPIpyqasm—indirect
PyPIpyqpanda3—indirect
PyPIpyqubo—indirect
PyPIpyquil—indirect
PyPIpytest—indirect
PyPIpytest-asyncio—indirect
PyPIpytest-cov—indirect
PyPIpytest-xdist—indirect
PyPIpytket—indirect
PyPIpytket-braket—indirect
PyPIpytket-cirq—indirect
PyPIpytket-qir—indirect
PyPIpytket-qiskit—indirect
PyPIqbraid-core—indirect
PyPIqbraid-qir—indirect
PyPIqcs-sdk-python—indirect
PyPIqiskit—indirect
PyPIqiskit-aer—indirect
PyPIqiskit-ibm-runtime—indirect
PyPIqiskit-ionq—indirect
PyPIqiskit-qasm3-import—indirect
PyPIqnexus—indirect
PyPIqrisp—indirect
PyPIrecurring-ical-events—indirect
PyPIrustworkx—indirect
PyPIsetuptools—indirect
PyPIsphinx—indirect
PyPIsphinx-autodoc-typehints—indirect
PyPIsphinx-copybutton—indirect
PyPIsphinx-rtd-theme—indirect
PyPIsympy—indirect
PyPItyping-extensions—indirect
Dependency advisories 0

Installing pypi:qbraid@0.13.0 pulls in 19 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

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Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.